Tuesday, August 12, 2014

In an Early Edition PNAS article published this week, Jason Rasgon and friends (with Grant Hughes as first author) publish an elegant set of experiments, the provocative results of which lead us to the conclusion: "THE MICROBIOME IS F-ING RESPONSIBLE FOR EVERYTHING" Honestly folks, is there one f-ing thing to which the microbiome does not contribute? Let's dive into this article, as it is some of the most interesting work I've seen on Wolbachia with direct relevance to their use in vector blocking. Oh, and if you want to read along (and have access to the PNAS site), here is the link to the article.

Some background first. Wolbachia, the alpha-proteobacterial queen of insect reproductive manipulations, is an obligately intracellular and maternally transmitted bacterium. Various folks have been investigating it for its ability to prevent mosquitoes (and other insects) from harboring or transmitting viruses (see earlier post on this here). Dr. Rasgon, and his lab, have been trying to infect the Malaria vector, Anopheles, with Wolbachia in order to take advantage of this sweet side-effect...it's been rough. Anopheles seem inordinately recalcitrant to Wolbachia infection -- very few infections found in the wild, difficulty establishing maternal transmission etc. However, it turns out, Wolbachia can infect Anopheles tissues ex vivo as well as cell lines, suggesting that there is something found in the whole organism that is preventing infection. Let's see if it's the microbiome!

The authors focus on using the Wolbachia strain wAlbB in this manuscript (from Aedes albopictus), since there is no native infection for Anopheles, this makes perfect sense. Normally, when Anopheles is infected with Wolbachia, they show severe defects in transmission and/or fecundity. In A. gambiae, the mosquitoes transmit the bacterium at very low titer and in A. stephensi, no progeny are obtained when mosquitoes are injected with Wolbachia.

In this work, the authors first show that antibiotic treatment actually allows Wolbachia to infect two different Anopheles species (gamiae (A) and stephensi (B) below), without the fecundity defects observed previously. They use qPCR (looking at the ratio between a Wolbachia gene - wsp - and a host gene s7).

Neat! Looks like antibiotic treatment allows Wolbachia to colonize. Now, most folks would say "job done!" at this point and publish the work as quickly as possible but Hughes et al. go further -- they try to figure out which microbiome member could be contributing to this difference and if that microbiome member is necessary and sufficient for the Wolbachia transmission defects and host induced phenotypes.

Next they do some basic amplicon work to identify microbial community members in these mosquitoes before and after antibiotic treatment. Interestingly, they find an increase in the prevalence of many major taxonomic groups after antibiotic treatment and a reduction in one major genus: Asaia.

Shamefully, I had never heard of this genus before reading it in this paper. It's a fascinating member of the Acetobacteraceae (or Acetic-acid bacteria) that associates with many different insects. It turns out that this bacterium is maternally transmitted and also culturable - leading many folks to think of it as a potential paratransgenesis tool for mosquitoes. But I digress. In the next set of truly elegant experiments, Hughes et al culture Asaia and generate antibiotic resistant mutants. They then add these back to the previously gnotobiotic insects and voila! Now these mosquitoes are unable to maintain a Wolbachia infectionand they die after a blood meal!

Above you see that supplementation with antibiotic-resistant Asaia plus a Wolbachia infection recapitulates the phenotypes originally observed in the mosquito pre antibiotic treatment. That is to say, that Asaia at least partially explains why Wolbachia has such a severe effect on the host. Ah, you ask, but the title of the paper is: "Native microbiome impedes vertical transmission of Wolbachia in Anopheles mosquitoes" not "Asaia plus Wolbachia kill mosquitoes." True dat. The authors do count Wolbachia titers after the addition of this interesting Acetic-acid bacterium and they found that these mosquitoes, when supplemented with Asaia, cannot transmit Wolbachia (qPCR results below showing infected offspring).

Brilliant! Clearly this second maternally transmitted symbiont protects its niche from Wolbachia and the collateral damage is the host. Although a tidy story, this study really provokes more questions and potential lines of inquiry into this system. For example, diversity actually increases when Asaia is removed from mosquitoes. Do any of these bacteria contribute to Wolbachia maintenance? What is the mechanism behind the host phenotypes induced by the Asaia-Wolbachia conflict? and Are Asaia-free mosquitoes more fit than those with Asaia? Looking forward to hearing more from this group!

Monday, July 28, 2014

My student, Fredrick ("Freddy") Lee, published his first research paper in Environmental Microbiology this month. Our lab's been focusing a lot recently on the honey bee microbiome and metabolic function of the community in situ. In this manuscript (titled: Saccharide breakdown and fermentation by the honey bee gut microbiome), we explore the capabilities of the honey bee gut community using RNA sequencing. We also tested the effect of some commonly used enrichment protocols (the MICROBEnrich kit from life technologies) in the pipeline Freddy used to process the bee samples. Using three individual bees, he extracted total RNA and also used the aforementioned kit. We made 100 cycle PE libraries for an Illumina GA IIx. Here are the highlights:Major bacterial phyla and classes identified using 16s rRNA gene mining
We used blast to map high quality short reads to our well known honey bee taxonomy and found that, like our previous studies, these transcriptomes are dominated by three major phyla (the Proteos, the Firmicutes, and the Actinobacteria). The major classes in our dataset were gamma-proteo, Bacilli and Actinobacteria (see Figure below).

If you're interested in "deeper" taxonomic classification, let me point out that short reads are not the best tool for this, as depending on the region they encompass, they can provide taxonomic resolution or not. Given that, here you can see the effect of using the MICROBEnrich kit on one of our samples (bee #2) - yay - more depth!

Now for the meat of the study: what exactly are those honey bee microbes doing?? The major signal we identified was carbohydrate utilization and within that, the major contributors were our three dominant classes of bacteria.

As there was already a metagenome published for the honey bee gut, we took advantage of that dataset and used it in combination with the metatranscriptome to put together a genomic + transcriptomic view of metabolism performed by the major bacterial classes. Interestingly, the majority of the pathways were corroborated by each dataset, even though they were collected from bees in different parts of the country, at different times, and likely different ages.

Oh, and in case you were wondering how our dataset mapped to the metagenomic one, here's a nice figure. Essentially, you can find blast hits between our contigs and the published metagenome, but due to variation in microbiome composition and the divergence of these bacterial taxa, we see a very large range of percent identities.

Support for the -omic predictions using community level profiling

One nice aspect to this work is that we went on to validate the -omic data using other kinds of data, in this case, community level profiling. Freddy went on to use BioLog Ecoplates to find evidence that the honey bee gut community could utilize the substrates predicted based on the metagenomic and metatranscriptomic data. In this assay, each well of a 96-well plate contains a single substrate. You essentially inoculate each well with your environmental sample (a mix of microbial members) and look for a color change as the tetrazolium salt in the well is reduced. Freddy incubated his plates at 37C under anaerobic conditions (we saw no color change for aerobic conditions). He did this for 10 individual bee guts and saw a surprising amount of variability bee to bee.

It's easy to overinterpret this kind of variability -- keep in mind that the biolog plates rely on viability of the microbe under those conditions, on serial dilutions from the environmental sample (which introduces variability) and just because you don't see a color change, doesn't mean the community isn't capable of that processing. However, overall, we found that the honey bee microbiome was capable of utilizing almost all the sugars provided, some of the amino acids, and other carbohydrate compounds. This may not be that surprising to some, as the honey bee diet is composed of sugars (such as those found in nectaries) and pollen. In future, we hope to identify which bacterial clades are responsible for each of these metabolic capabilities and which products of metabolism each produces. Understanding how these bacteria interact through co-metabolism of honey bee food will be critical to our understanding of how the microbiome, and dysbiosis of the microbiome, contribute to honey bee health.

Wednesday, April 16, 2014

A truly awesome paper on Wolbachia, variation in the pathogen-blocking phenotype, and genetics was published a while back by Luis Teixeira's group. I've been eager to write a blog post on this particular paper, one of my favorite papers from 2013, so here goes! <Follow along with the paper here.>You are reading this blog, so maybe you don't need convincing that Wolbachia are totally awesome, relevant, and interesting bacteria. They infect about half of the insect species on the planet and do so by targeting the germ line: that's right folks, these babies come pre-loaded with their bacterial symbiont. Recently, Wolbachia have become more medically relevant because folks (including Texieira himself with Michael Ashburner) found out that they protect their insect hosts from virus infection -- either by reducing the load that the host carries (resistance) or by preventing disease even if the virus replicates (tolerance). However, different Wolbachia strains vary in their ability to pathogen block.In Chrostek et al., (2013), the authors attempt to figure out what's behind this variability -- why do some Wolbachia strains protect their hosts better than others? Is this variability consistent across virus strains? These are relevant questions to ask because 1) it may reveal the underlying biology of Wolbachia-host interaction and 2) it may lead us to different Wolbachia strains that could be utilized in pathogen blocking. The authors start by sequencing some variants of Wolbachia (they also use previously published whole genome data from Casey Bergman's group) and by figuring out if these variants differ in their ability to protect the host from Drosophila C virus. Just because I'm a big fan of BIG trees - here's the phylogeny resulting from their genomic analysis.

Figure 1

Interestingly, the variants do differ. In Figure 2A (below), you can see that each of the strains provide differing degrees of protection, with the uninfected line (iso) dying quite shortly after infection. They also repeated this experiment with Flock house virus with similar trends (see Figure 3).

Figure 2 from Chrostek et al., 2013

Next, they look at the density of Wolbachia variants within the same host background. Interestingly, they found that wMelCS variants exist at MUCH higher titers than wMel variants. In Figure 4 below, you can see the results of qPCR on Wolbachia genomic DNA from flies that are 3-4 or 6-7 days old. Strikingly, you see a huge upward trend in wMelCS infection as the flies age (Figure 4D), but no so much for wMel. Interestingly, some of these wMelCS variants reduce host lifespan!

Figure 4 from Chrostek et al., 2013

So...there must be some difference in these Wolbachia strains. Chrostek et al were quite careful in their crosses -- they removed confounding variables such as the non-Wolbachia microbiome and host genetic background. So, they look at potential genomic differences in these strains -- remember, they sequenced the genomes to characterize their Wolbachia into the different variant clades. They present a very large table of SNPs in the pairwise comparisons ... some interesting looking genes with ankyrin repeat domains...potentially cool stuff for future work from this group. BUT! The rub is that none of these indels are found in wMelPop, the variant that protects BEST against viruses and infects at highest titer (is the most pathogenic to the fly). So...what else could be different? What is driving the titer differences in these variants?Could be copy number variation be driving the difference between the strains? Indeed so! The authors identify a region in wMelPop, containing 8 genes, that is elevated in copy number (between ~2-8x) compared to the other wMel variants. In a brilliant stroke of creativity (or as a result of a potential late night pub crawl), the authors name this region the "Octomom" region (see below):

Figure 7: So called "Octomom" region increase in coverage in the wMelPop genome (A) and qPCR based amplification in wMelPop vs other variants (B)

This region has some interesting genes in it, some of which are phage related, some of which have homologs to mosquitoes! Although we don't know what these genes are doing, these proteins could be of interest for those researchers interested in Wolbachia pathogenesis. < Oh, and also, this entire body of work is all in the context of the global replacement of certain Wolbachia strains in Drosophila melanogaster, as it turns out. >

Tuesday, February 11, 2014

Whether you are reading a review of a grant application or that infamous "reviewer #3's" response to your manuscript, rejection can be tough to handle. Listening to music may be able to help you cope with this stressful event <see research on this phenomenon here>, perhaps enough to get you past this submission cycle with enough cojones for the next. Here are some suggestions, take 'em or leave 'em:

"Black" by Pearl Jam.
Just so obviously appropriate. Although maybe love song for NIH/NSF. "Yeah. I know someday you'll have a beautiful life . I know you'll be a star. In somebody else's sky. But why. Why. Why can't it be. Why can't it be mine." Why can't that $$ be mine...

"Mr. Self Destruct" by Nine Inch Nails
I bring you the 90's Trent Reznor, sigh, what a dreamboat "I take you where you want to go. I give you all you need to know. I drag you down I use you up Mr. Self-destruct"

"One Step Closer" by Linkin Park
"Everything you say to me takes me one step closer to the edge...and I'm about to break". There's some wisdom in that...develop a thick skin early for the review process.

Phase 2: Anger - what the f**k do they know, anyway?
"F**k You" by Cee Lo Green
This song should be a classic. And although this is a song about personal rejection, I think that the chorus itself resonates: "I've got some news for you. Ooh, I really hate your a** right now"

"The Way I Am" by Eminem
"I am not Mr. Friendly" and "No patience is in me." This how you feel?

"Karma Police" by Radiohead
"This is what you get when you mess with us". If only it were an open review...

Phase 3: Empowerment (or self-delusion) -- you ARE the best...you just don't have a grant..or that pub..YET

"Not Afraid" by Eminem
"We'll walk this road together, through the storm, whatever weather, cold or warm. Just letting you know that you're not alone"..that's for damn sure. With pay lines as meager as they are, you are certainly part of a big club!

Tuesday, January 28, 2014

We've been looking for ways to analyze transcriptomes correctly, with sufficient power, not too many type I and II errors, and not much fuss. For those relatively unfamiliar with performing differential expression analyses on RNA-seq data, a great review of the statistical methods employed to analyze these data can be found here.

What it all comes down to is the fundamental problem associated with RNA seq experiments -- the absence of a single transcript could be due to down regulation OR, could be due to the up regulation of ANOTHER gene. That's right, what you are measuring are RELATIVE expression levels, and given libraries of the same size, you cannot accurately distinguish the first scenario from the second unless you've spiked the libraries with some standards of known quantity (which, interestingly enough, has been done before with success by Mary Ann Moran's group here).

From Mary Ann Moran's paper on the subject, we have this very nice depiction of the problems associated with sampling depth, relative number of reads, etc.:

It is very difficult to distinguish between samples 1 and 2 unless you can take into consideration library size or know, using internal standards how number of reads translates to number of copies. Even if you use internal standards, it should be noted that RNAs have varying half-lives due to their own specific secondary structures, potential protective modifications, etc. Therefore, there will always be some stochasticity associated with the sampling that will reverberate in your final counts of reads.

I have been playing with the program RSEM to calculate both FPKM (fragments per killobase per million mapped reads, = [# of fragments]/[length of transcript in kilo base]/[million mapped reads]) and TPM (transcripts per million mapped reads) values. In the RSEM publication, the authors convincingly argue that the TPM metric is a much better way of comparing between libraries -- much better than RPKM (or FPKM) alone. The reason? Libraries are not all of the same size and it is necessarily the case that an increase in expression of any particular gene in one library will lead to the exclusion of other genes. Also, RSEM uses a statistical model to take into account the uncertainty associated with read mapping - especially in transcriptomics where multiple isoforms exist. Oooh... also, RSEM doesn't require a reference genome -- awesome!

RSEM's output provides both FPKM values as well as the TPM values, an estimated fraction of ttranscripts made up by a given gene. I was curious to know how each of these measures would perform on environmental data -- one would assume that they would be correlated! I used RSEM (-rsem-calculate-expression
–calc-ci –paired-end) on a set of illumina libraries and found...

that FPKM and TPM values are amazingly well correlated; within a library, sorting by FPKMs or TPMs will give you the same result. But, what happens when you compare between libraries? Same answer. At least in the data I used, comparing two libraries using FPKM or TPMs results in the same answer with regards to differential expression. That said, I rest easier knowing that for the TPM values generated RSEM also provides 95% confidence intervals, helping me to better assess statistical differences between libraries.

In all that spare time you have, you can compare Edge-R's gene-specific bayesian modeling found here to RSEM, the statistical software I'll be exploring today found here.
Oh, and here's another nice review, http://www.biomedcentral.com/content/pdf/gb-2010-11-12-220.pdf

Monday, January 20, 2014

I recently had the pleasure of finally sitting down to read some publications (both open access!) on my favorite bacterium, Wolbachia pipientis. These recent pubs interested me because they focused on the population genetics of Wolbachia within individual hosts, upon host transfer, and after many generations. The BIG question that comes out of this body of work, in my mind, is how are low-titer strains in the maternally transmitted population maintained! (We can discuss ongoing hypotheses at the end of this post)

The first paper I'll tackle (Schneider et al) asks if Wolbachia strains exist as diverse quasi-species within a host and reveals that diversity using host transfer techniques. In "Uncovering Wolbachia Diversity upon Artificial Host Transfer" by Schneider et al., the authors use the cherry fruit fly Wolbachia (wCer strains) as the inoculum for injection of two new hosts: Drosophila simulans or Ceratitis capitata. For those unfamiliar with the technique, what it comes down to is harvesting many many embryos from your D. simulans, using differential centrifugation techniques to concentrate the Wolbachia fraction and using that, as you would in microinjection of a construct to make transgenic flies.

The cool thing about this paper is that they see cryptic polymorphisms rise after host transfer. They looked at 150 generations after microinjection and saw a low titer variant increase in frequency such that it was detectable via PCR. Now, the data in this paper is entirely PCR based -- they sequenced amplifed fragments and used them to detect SNPs. That said, if found to be true, it suggests that the host and symbiont evolve really rapidly and that Wolbachia maintains diversity, even under conditions when it should be primarily maternally transmitted (lab stocks).

The second paper I'm highlighting (Symula et al., 2013) investigated the diversity of Wolbachia in tsetse fly populations and correlated Wolbachia haplotypes with specific host mtDNA haplotypes. Their result = LOTS of Wolbachia diversity and evidence that these infections happened independently, multiple times. The authors collected tsetse flies across a region in Africa and did an analysis of the Wolbachia MLST genes and groEL - they also looked at host mtDNA haplotypes. Again, they used PCR amplification and sequencing but were VERY conservative in their sequence post-processing (removing all recombinants, for example). So, the data they present are potentially a lower bound estimate of Wolbachia diversity. The number of haplotypes found within each host was astounding (see Table 1). In some cases, 6 different haplotypes found within just 2 hosts!

Mechanisms for maintaining genetic diversity in a maternally transmitted symbiont?

1) Bend the rules:
During my doctoral work, a lab member discovered that there was cryptic diversity within the maternally transmitted endosymbionts of the deep sea clams. In that work, they discovered that a low frequency (0.02) symbiont haplotype existed in a population of clams that were geographically localized. It was hypothesized there that the trick to maintaining diversity in this maternally transmitted symbiont was to basically bend the rules: occassionally, transmit your symbiont horizontally. Since we find evidence of horizontal transmission in Wolbachia, this is one mechanism that genetic diversity could be maintained in the population.

2) Increase mutation rates:
It would be theoretically possible for an endosymbiont to have such rapid rates of mutation that individual populations within a single host would exhibit variability detectable by the methods employed by Schneider et al. and Symula et al. Evolutionary rates are elevated in endosymbionts, so this is a potential source of new genetic diversity for Wolbachia.

It will be quite interesting to see which (or if both?) of these scenarios play a role in Wolbachia genomic evolution. These changes in symbiont population dynamics and densities could potentially allow Wolbachia to colonize new hosts, potentially acting as a quasi-species (as seen in virus systems).